Object instance identification using template textured 3-D model matching

ABSTRACT

Systems, methods, and articles of manufacture for automatic target recognition. A hypothesis about a target&#39;s classification, position and orientation relative to a LADAR sensor that generates range image data of a scene including the target is simulated and a synthetic range image is generated. The range image and synthetic range image are then electronically processed to determine whether the hypothesized model and position and orientation are correct. If the score is sufficiently high then the hypothesis is declared correct, otherwise a new hypothesis is formed according to a search strategy.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

The invention described herein may be manufactured and used by or for the government of the United States of America for governmental purposes without the payment of any royalties thereon or therefor.

FIELD OF THE INVENTION

Embodiments generally related to automatic target recognition systems and methods.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates components of some system and/or article of manufacture embodiments.

FIG. 2 provides an exemplary high-level flow-chart/diagram of steps performed during some embodiments of the invention.

FIGS. 3A and 3B provide an exemplary flow-chart/diagram of steps performed during some embodiments of the invention.

FIG. 4 provides an exemplary flow-chart of color-rendering steps performed in some embodiments.

FIG. 5 illustrates an exemplary range image of an exemplary scene including an embodiment of a target.

FIG. 6 illustrates an exemplary synthetic range image of a scene including an embodiment of a target.

FIG. 7 illustrates an exemplary synthetic range image of a scene including an embodiment of a target.

FIG. 8 illustrates various exemplary range images, each of a different scene, and various exemplary synthetic range images.

FIG. 9 provides an exemplary diagram of steps employed in generating a synthetic range image according to some embodiments of the invention.

FIG. 10 provides an illustration of components that can be used to either collect/process/receive data used or manipulated during some embodiments of the invention.

It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not to be viewed as being restrictive of the invention, as claimed. Further advantages of this invention will be apparent after a review of the following detailed description of the disclosed embodiments, which are illustrated schematically in the accompanying drawings and in the appended claims.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

At the outset, it is perhaps helpful to describe conventions and input.

Input

Embodiments of the invention use the following input data generated conventionally unless otherwise stated: 1) sensor range image; 2) target information; 3) initial target search parameter extents; 4) target model (3-D wiregrid model) to be used during the current model iteration set; 5) k; 6) L; 7) l; 8) d (where d is a factor less than or equal to 1); 9) h; 10) w; 11) matching score threshold value; 12) var1 value; 13) var2 value; 14) var3 value; 15) sensor intensity image. ‘h’ is a value from the range of values between and including zero and four; w is selected from the range of values between and including zero and four. k and L are set experimentally and are a function of the range noise of said sensor and object modeling errors. L is a value (in meters) in the range of (and including) 0 to 10, depending on the fineness of the sensor. k is a value in the range of (and including) 0 to 7. l is set to the degree to which a normal distributed random variable is desired; it serves as an index (with a lower value of greater than or equal to 1), and as the value of l gets larger, the distribution becomes more and more normal.

In some embodiments, L=3 and k=0. In some of these embodiments, h=1 and w=1.

In some embodiments, L=8 and k=7. In some of these embodiments, h=1 and w=1.

The value of var2 is greater than the value of var3 and var1 value is greater than L. In some embodiments, var 2 is 1, var3 is 0, and var1 is the value to which the depth buffer was initialized.

Range Imaging Sensor

A range-imaging sensor is any combination of hardware and software capable of producing a range image of a real-world scene under appropriate operating conditions. In some embodiments, a Laser Detection and Ranging (LADAR) sensor is used to generate the range data from which a range image is generated.

Optical Sensor (Intensity Imaging Sensor)

An optical sensor or (Intensity imaging sensor aka image sensor) is a device that converts an optical image into an electronic signal. It is used mostly in digital cameras, camera modules and other imaging devices. Early analog sensors were video camera tubes; most currently used are digital charge-coupled device (CCD) or complementary metal-oxide-semiconductor (CMOS) active pixel sensors. The intensity imaging sensor is typically co-located with the Range Imagine Sensor.

Search Parameter Extent

A “search parameter extent” is the extent of search space assigned to the parameter for the current iteration, and is notationally referred to using the letter ‘e’ with a subscript g relating the parameter extent values to the hypothesis with which it will be used (or during the iteration of which it was calculated). A parenthetical following the ‘e’ indicates the search parameter the value is associated with. For example, using the ‘g’ scheme in FIG. 3, e_(g)(a) would indicate the search extent value of the a^(th) search parameter calculated during the g^(th) hypothesis. As a notational note, e_(g)={e_(g)(a), a=1 . . . b, where b is equal to the number of search parameters.

Initial Search Parameter Extent (Input)

The initial search parameter extent may be selected based on a-priori knowledge of target performance. For example, if the set of potential target models is a set of different sea-going vessels, an initial search parameter extent for the iteration's pitch parameter may be selected based on a-priori knowledge that sea-going vessels tend to experience pitch variations within plus or minus 5 degrees. Therefore, the initial search parameter extent for pitch in this example would be set to e=5. The manner of setting the initial search parameter extents is not unique to this patent; any conventional means of arriving an initial search parameter extent may be used.

Range Image

A range image is an image of a physical scene represented in an electronic data structure as a collection of electronic distance measurements (electronic data) from a known reference coordinate system to surface points on object(s) in the scene. If scenes are defined as collections of physical objects and if each object is defined by its mass density function, then surface points are defined as the 3-D points in the half density level set of each object's normalized mass-density function as in Koenderink and VanDoorn (1986). Range images are known by many other names depending on context: range map, depth map, depth image, range picture, rangepic, 3-D image, 2.5-D image, digital terrain map (DTM), topographic map, 2.5-D primal sketch, surface profiles, xyz point list, contour map, and surface height map. If the distance measurements in a range image are listed relative to three orthogonal coordinate axes, the range image is in xyz form. If the distance measurements indicate range along 3-D direction vectors indexed by two integers (i, j), the range image is in r_(i,j) form. Any range image in r_(i,j) form can be converted directly to xyz form, and the x,y,z form as used to represent the target model are converted into the range image r^(i,j) form. Since no ordering of points is required in the xyz form, this is the more general form, but it can be more difficult to process than the r_(i,j) form. If the sampling intervals are consistent in the x- and y-directions of an xyz range image, it can be represented in the form of a large matrix of scaled, quantized range values r_(i,j) where the corresponding x, y, z coordinates are determined implicitly by the row and column position in the matrix and the range value. The term “image” is used because any r_(i,j) range image can be displayed on a video monitor, and it is identical in form to a digitized video image from a television camera. The only difference is that pixel values represent distance in a range image whereas they represent irradiance (brightness) in a video image.

For the purposes of this description, range images are described as being operated on in the r_(i,j) form, but can easily be converted to xyz range form using a standard transformation matrix and offsets.

Sensor Range Image

A “sensor range image” is a range image of a scene that includes a detected target. The sensor range image is notationally defined as R={r_(ij), i=1, . . . b, j=1 . . . c}, where b is the number of columns of pixsels in said sensor target range image, c is the number of rows of pixels in said sensor range image, and r_(ij) represents the range between the object at point ij in the sensor range image and the sensor. The scene coordinate frame of reference is the coordinate system with respect to which range values in the range image are calculated.

FIG. 5 illustrates an example of a range image 502 of a target, in this example a tank 54.

FIG. 10 illustrates a general set up to explain the cooperation of certain elements that are used to either collect/process/receive data used or manipulated during some embodiments of the invention. In particular, 112 a and Ii 2 b illustrate co-located LADAR and intensity sensors, respectively, that receive scattered energy returned by an object (in this example a target tank 110)—in the case of LADAR sensor 112 scattered energy transmitted by LADAR transmitter 150. Navigation unit 152 is co-located with LADAR sensor and provides the location and orientation of LADAR sensor 112 a and intensity sensor 112 b at the time the target tank 110 was imaged. The location and orientation information from the LADAR is used to convert the range image data from LADAR sensor 112 into a world based coordinate system, thereby associating the range data from LADAR sensor with world coordinates.

Intensity Image

Intensity images measure the amount of light impinging on a photosensitive device. The input to the photosensitive device, typically a camera, is the incoming light, which enters the camera's lens and hits the image plane. In a digital camera, the physical image plane is an array which contains a rectangular grid of photosensors, each sensitive to light intensity. The output of the array is a continuous electric signal, the video signal. The video signal is sent to an electronic device called frame grabber, where it is digitized into a 2D rectangular array of integer values and stored in a memory buffer.

The interpretation of an intensity image depends strongly on the characteristics of the camera called the camera parameters. The parameters can be separated into extrinsic and intrinsic parameters. The extrinsic parameters transform the camera reference frame to the world reference frame. The intrinsic parameters describe the optical, geometric and digital characteristics of the camera. One parameter, for example, can describe the geometric distortion introduced by the optics.

Sensor Intensity Image

A “sensor intensity image” is an intensity image of the range image scene taken by the intensity imaging sensor. The sensor range image is notationally defined as I={I_(ij), i=1, . . . b, j=1 . . . c}, where b is the number of columns of pixels in the sensor intensity image, c is the number of rows of pixels in the sensor intensity image, and I_(ij) represents the amount of light impinging on pixel ij of photosensitive device (sensor) that produced the intensity image.

Target Pose Parameters

“Target pose parameters” are parameters whose values describe the pose of the detected target in the scene coordinate frame of reference. A translation relates the pose of target models in the model coordinate frame of reference to a corresponding pose in the scene coordinate frame of reference. The target pose parameters for a particular hypothesis are collectively notationally represented as P_(g). Parenthetical (a) identifies a parameter specific value. So, for example, where the target pose parameters are comprises of x, y, z, roll, pitch, and yaw (6 parameters in their respective order) the target pose parameters for a first hypothesis (g=1) would be notationally represented as P₁={P₁(1),P₁(2),P₁(3),P₁(4),P₁(5),P₁(6)}, where P₁(1), P₁(2),P₁(3) represent the target pose parameter for the target's x, y, and z-position, respectively, for the current hypothesis, and P₁(4),P₁(5),P₁(6) represent the target pose parameter values for the target's roll, pitch and yaw for the current hypothesis.

Notice that target velocity can also be modeled by repositioning the target model as a function of time. Where this is done, the sensor range image would be notationally defined as R={r_(tij), t=1 . . . u, i=, . . . b, j=1 . . . c} and the synthetic range image would be defined as R^(s)={r_(tij) ^(s),t=1 . . . u, i=1, . . . b, j=1 . . . c}. Equations 4, 6, and 8 would likewise change as follows, respectively:

$a_{g} = \frac{\sum\limits_{t = 1}^{u}\;{\sum\limits_{i = 1}^{b}\;{\sum\limits_{j = 1}^{c}\;\begin{matrix} {{r_{tij}^{s,g} - {r_{itj}\mspace{14mu}{if}\mspace{14mu} f_{tij}^{s,g}}} = {true}} \\ {{0\mspace{14mu}{if}\mspace{14mu} f_{tij}^{s,g}} = {false}} \end{matrix}}}}{\sum\limits_{t = 1}^{u}\;{\sum\limits_{i = 1}^{b}\;{\sum\limits_{j = 1}^{c}\;\begin{matrix} {{1\mspace{14mu}{if}\mspace{14mu} f_{tij}^{s,g}} = {true}} \\ {{0\mspace{14mu}{if}\mspace{14mu} f_{tij}^{s,g}} = {false}} \end{matrix}}}}$ $d_{t,i,j}^{g} = \left\{ {{\begin{matrix} {{Minimum}\left( \sqrt{\left( {\left( {r_{t,i,j}^{S,g} - {a_{g}d}} \right) - N_{ij}} \right)^{2}} \right)} & {{{if}\mspace{14mu} f_{t,i,j}^{S,g}} = {true}} \\ {{var}\; 1} & {{{if}\mspace{14mu} f_{t,i,j}^{S,g}} = {false}} \end{matrix}C_{g}} = {\sum\limits_{v = 0}^{k}\;{\sum\limits_{t = 1}^{u}\;{\sum\limits_{i = 0}^{b}\;{\sum\limits_{j = 0}^{c}\;\left\{ \begin{matrix} {{var}\; 2} & {d_{i,j}^{g} < T} \\ {{var}\; 3} & {d_{i,j}^{g} \geq T} \end{matrix} \right.}}}}} \right.$ Target Search Parameters

“Target search parameters” are parameters of the “target pose parameters” whose values will be considered for variation during a model hypotheses set. In some embodiments, the hypotheses are limited to variation across only one or some of the target pose parameters; values for the parameters across which the hypotheses are allowed to vary are referred to herein as the target search parameters values and notationally collectively represented as P_(s). Values for target pose parameters that are fixed during the model hypotheses set are referred to as “constant pose parameters” and are notationally referred to as P_(c), such that P_(g)={P_(s),P_(c)}.

For example, in some embodiments, model iteration sets are only allowed to vary across position; in these embodiments, the orientation (roll, pitch, and yaw) parameter values of the hypotheses would remain constant during each iteration of the model iteration set and the “search parameters” would consist only of parameter values describing location. Where ‘a’ is in parenthesis, such as P_(s)(a), ‘a’ represents an index that identifies the search-parameter specific value. For example, where there are three search parameters—x, y, and z position—P_(s)(1) would be the search parameter value for the x position, P_(s)(2) would be the search parameter value for the y position, and P_(s)(3) would be the search parameter value for the z position.

Hypothesis-specific search parameter values are identified using sub-subscript ‘g’, such as P_(s) _(g) (a), which would designate a hypothesis-and-search parameter-specific search parameter value. Using the above search parameter examples, P_(s) ₂ (1) would designate the parameter value of the first search parameter of the second hypothesis.

Rendering

In general, given a 3D scene model G, a pose P, and a sensor type, one can compute the image that would be recorded by a sensor of that type from the specified pose. This computation is referred to as “rendering”.

i. Range Rendering

A range sensor provides depth information, i.e., the distance from the imaging device. For a range sensor, the resulting rendered image is sometimes referred to as a “synthetic range image.” In this case, rendering can be carried out using a hardware Z-buffer, which is computationally efficient. Alternatively, the rendering can be carried out by software. The process is referred to as “range rendering.” The field has an extensive literature and range rendering using conventional techniques is known to a person having ordinary skill in the art.

In some embodiments, range rendering is performed by a graphics processing unit programmed to perform the steps/acts/processes as illustrated in FIG. 9. With reference to FIG. 9, these embodiments render the synthetic range image by first defining sensor parameters for the sensor model that is used to create the simulation that represents the range image (block 902). In particular, this step involves defining pixels by width and height based on a degree value and the field of view of the modeled sensor

With reference to block 904, a pulse is then initialized by configuring the pulse/energy according to constraints imposed by the model sensor parameters Namely: 1) the energy transmitted by the sensor model is defined to be transmitted in the same direction (in the scene coordinate frame of reference) as the actual sensor is looking so that a return will be measured; and 2) the pulse dimensions are defined to be equal to the angular dimensions of the field of view of the actual sensor.

Next, a scene model (background and target model) is initialized using the 3-D target model of the current model hypotheses set (block 906) in the model frame of reference.

Next, with reference to block 908, location and orientation of the actual sensor is received from the navigation unit, (which outputs xyz location and orientation of the actual sensor in the “scene coordinate system frame of reference”), and uses this information to define the xyz location and orientation of the modeled sensor in the scene coordinate system frame of reference.

Next, with reference to block 910 the scene location and orientation is received. In other words, the scene model is modeled with the target model according to the P_(g) for the current hypotheses. Recall that P_(g) defines the pose of the scene (target model) for a particular hypothesis in the scene coordinate frame of reference. At this point, the imaged scene model is defined in xyz form in the scene coordinate frame of reference.

Next, with reference to block 912 depth buffer data of the scene model (the model at Pg) is generated. The depth buffer is initialized to an initial value. All pixels that remain unchanged retain the initial depth buffer value (which allows them to be distinguished from pixels that lie on the target). The depth-buffer data is an array of depth values, one depth value for each pixel of the sensor focal plane array. This computer graphics hardware processing can be invoked and results obtained using OpenGL by calling the glReadPixels function. Note that there is one depth-buffer pixel for each detector in the sensor focal plane array.

Next, with reference to block 914, the depth-buffer data is transformed into range data, which defines the synthetic range image. Block 914 reads the depth values from the depth-buffer computed with the computer graphics hardware and transforms them into values which have linear units of length. The depth-buffer is read using OpenGL as:

glReadPixels(0, 0, width, height, GL_DEPTH_COMPONENT, GL_FLOAT, depthbuf);

where, width is the width in pixels and height is the height in pixels of the LADAR focal plane array and of the depthbuf array which is where the depth-buffer computational result is obtained on call return.

ii. Color Rendering

With reference to FIG. 4, color rendering 80 involves receiving intensity imagery of the target being hypothesized as a match 91. In some embodiments, the intensity imagery is in the form of multiple digital images (photographs) of the object represented by the 3-D wire grid model or “target model” of the current hypothesis. A correlation filter, e.g., “template image” 92 (such as a MACH filter) is generated from the training intensity imagery. Correlation filters are well known in the art, and are generally generated using a set of training images (taken by the intensity sensor of the target you're trying to match) consisting of the expected range of rotation using any known technique. Generating correlation filters is well known in the art.

The color rendering process also involves texturing the target model 96 with the template image 92 at a common orientation using any known technique, thereby producing what is referred to as a “template-textured 3-D model” 98. The template textured 3-D model is rendered at the current hypothesis target pose parameters Pg 99 resulting in a 2-D image referred to herein as a “synthetic intensity image”.

Synthetic Range Image

The term “synthetic range image” describes electronic range data representing the physical scene formed by range rendering a target model at a particular pose during analysis of a current hypothesis. The synthetic range image of a particular iteration ‘g’ is notationally referred to as R^(S,g) where g represents the hypothesis (and corresponding pose parameter values) at which the target model of the current model hypotheses set will be rendererd. Note that the synthetic range image is of the same size (pixel-wise) as the sensor range image. A synthetic range image is mathematically described by Equation 1, where a pixel at location ij in the synthetic range image is notationally represented herein as f_(ij) ^(S) ^(range) =true if the pixel is on the rendered 3-D target model in the synthetic range image as f_(ij) ^(S) ^(range) =false if it lies off the rendered 3-D target model in the synthetic range image. R ^(s) ={r _(ij) ^(s) , i=1, . . . b, j=1 . . . c}  Equation 1

Hypothesis-specific synthetic range images are notated using the index ‘g’ to represent the hypothesis the synthetic range image is used to test, as in Equation 2. R ^(s,g) ={r _(ij) ^(s,g) , i=1, . . . b, j=1 . . . c}  Equation 2 A pixel at location ij in the synthetic range image of a ‘g^(th)’ hypothesis is notationally represented herein as f_(ij) ^(S) ^(range) ^(, g)=true if the pixel is on the rendered 3-D target model in the synthetic range image of the ‘g^(th)’ hypothesis and as f_(ij) ^(S) ^(range) ^(, g)=false if it lies off the rendered 3-D target model in the synthetic range image of the ‘g^(th)’ hypothesis. For example, R^(s,1) would represent the synthetic range image rendered for the first hypothesis of a model hypothesis set, and it would be formed of range values as mathematically represented in Equation 3. R ^(s,1) ={r _(ij) ^(s,1) , i=1, . . . , j=1 . . . c}  Equation 3

FIGS. 6 and 7 illustrate synthetic range images of different target models. 602 is a synthetic range image that includes a rendered target model 604 of an exemplary tank. 702 is a synthetic range image that includes a rendered target model 704 of a different tank than 604.

With reference to FIG. 8, 800 illustrates an exemplary sensor range image including an exemplary target 801. 804A, 806A, and 808A are exemplary synthetic range images of scene 800 including different target models 805, 807, 809 at different poses. 802 illustrates an exemplary sensor range image including an exemplary target 803. 804B, 806B, and 808B are exemplary synthetic range images of scene 802 using the same models as used in the corresponding exemplary synthetic range images of scene 800, but at different poses. For example, synthetic range images 804A and 8048 render images including target model 805, but at different poses (805 in synthetic range image 804A is at a different pose than 805 in FIG. 804B).

Synthetic Intensity Image

The term “synthetic intensity image” describes electronic intensity data representing the physical scene formed by color rendering the target model at a particular pose during analysis of a current hypothesis (see above for color rendering description). The synthetic intensity image of a particular iteration ‘g’ is notationally referred to as R^(S,g) where g represents the hypothesis (and corresponding pose parameter values) at which the template textured 3-D model of the current model hypotheses set will be rendererd. Note that the synthetic intensity image is of the same size (pixel-wise) as the sensor intensity image. A synthetic intensity image is mathematically described by Equation 4, where a pixel at location ij in the synthetic range image is notationally represented herein as f_(ij) ^(S) ^(color) =true if the pixel is on the color-rendered 3-D target model in the synthetic intensity image and as f_(ij) ^(S) ^(color) =false if it lies offthe color-rendered 3-D target model in the synthetic intensity image. T ^(s) ={t _(ij) ^(s) , i=1, . . . b, j=1 . . . c}  Equation 4

Hypothesis-specific synthetic range images are notated using the index ‘g’ to represent the hypothesis the synthetic range image is used to test, as in Equation 5. T ^(s,g) ={t _(ij) ^(s,g) , i=1, . . . b, j=1 . . . c}  Equation 5 A pixel at location y in the synthetic intensity image of a ‘g^(th)’ hypothesis is notationally represented herein as f_(ij) ^(S) ^(color) ^(,g)=true if the pixel is on the rendered 3-D target model in the synthetic intensity image of the ‘g^(th)’ hypothesis and as f_(ij) ^(S) ^(color) ^(,g)=false if it lies off the rendered 3-D target model in the synthetic intensity image of the ‘g^(th)’ hypothesis. For example, T^(s,1) would represent the synthetic intensity image rendered for the first hypothesis of a model hypothesis set, and it would be formed of intensity values as mathematically represented in Equation 6. T ^(s,1) ={t _(ij) ^(s,1) , i=1, . . . b, j=1 . . . c}  Equation 6 Target Information (Input)

The “target information” input includes information describing pose (referred to as “pose information input”) and identity (or class type) of a recognized target localized using a known, well-known, or conventional method or system. The “detected target” is a collection of pixels in the sensor range image that collectively comprise the target detected within the range image of the imaged scene.

The input pose information is notationally referred to as P_(input). The input pose information defines an estimated pose of the detected target within the sensor range image via a set of target pose parameters. Though the information regarding initial models and pose are considered input and may be either arbitrarily selected or derived using any known, well-known, or conventional process, we consider generation of the input in some detail as follows. Where conventional, known, or well-known range image processing is used to provide target detection and pose information input, the input is typically generated by performing segmentation, feature extraction, recognition and localization.

The process of recognition determines the identity of a target instance. Regarding recognition, the object (or target) instances in a 3D scene model may be of various types. One type is parameterized shape models, e.g. spheres, cylinders, cuboids, or superquadrics. A second type is arbitrarily-shaped object instances that match object models in a library of object models. In this case, each model in the library describes a specific object. Recognizing object instances found in such a library is referred to as “object recognition”, see Chapter 10 of E. Trucco and A. Verri, Introductory Techniques for 3-D Computer Vuion, Prentice Hall, 1998; also see Shimon Ullman, High-level Vision, The MIT Press, 1997 A third type is arbitrarily-shaped object instances that match class models in a library of class models. In this case, each model in the library describes a class of related objects, e.g. chairs, trucks, cars, barns, etc. Such a class model is sometimes called a “class prototype”. In various embodiments, the present invention can be applied to each of these types of object instances, as described below.

Localization is typically performed by computing the transformation from a model (or class prototype) coordinate frame of reference to the scene coordinate frame of reference that would place the object model in registration with the object in the scene (detected target). Localization results in the initial pose estimate for the target within the scene.

In other embodiments, the initial pose parameters are set equal to the x,y,z location of the average position of the pixels that comprise the detected target in the range image. Where this is employed, the range image is converted from the r_(i,j) form to the xyz range form using a standard transformation matrix and offsets, and the x, y, z values of the pixels that comprise the detected target are averaged. In these embodiments, the initial roll, pitch, and yaw values are selected based on a-priori knowledge of the environment, type of object, and potentially intel. For example, if we know that the object is a land based vehicle on the ground, we might set the roll to zero because land based vehicles are less likely to experience significant roll.

Target Model (Inut)

A “target model”, as used herein, is a 3-D wire grid model of a potential target match (to the detected target) modeled in a model coordinate frame of reference (set model origin to the middle of its extent). The target model for a model hypotheses set describes the model to be used during the current model hypotheses set. The “target model input” is information that identifies a selection of one of the models within the model set for the current iteration. In embodiments in which multiple models are contemplated as potential matches, a model is iteratively analyzed until a decision is made to move on to a different object model in the model set. The collection of iterations performed (hypotheses tested) on a single model is referred to collectively as a “model iteration set”.

To avoid convoluting the description with reference to models in different model hypotheses sets, the remainder of the detailed description focuses on description of iteration through a single model hypotheses set, but it is understood that some embodiments include performing multiple model hypotheses sets, each on a different model within a model set.

Target Hypothesis

A target hypothesis for the purposes of this description and claims refers to a particular set of values of target pose parameters (position and orientation).

Geometrical Matching Score Threshold Value

The matching score threshold value is statistically based on test trials (or training). The value of this threshold can be set based on how finely it is desired to differentiate between objects. Alternatively, the threshold can represent a percentage match. For example, where a threshold score represents a required 70 percent match, the percentage would be calculated by dividing the matching score by the sum of k, b, and c in Equation 9.

Color Matching Score Threshold Value

The matching score threshold value is statistically based on test trials (or training). The value of this threshold can be set based on how finely it is desired to differentiate between objects. Alternatively, the threshold can represent a percentage match. For example, where a threshold score represents a required 70 percent match, the percentage would be calculated by dividing the matching score by k in Equation 13.

Apparatus Embodiments

With reference to FIG. 1, system embodiments include a Central Processing Unit 2 operatively associated with electronic memory 4 and a graphics processing unit 6, operatively associated with a display monitor, to display a visual depiction of the range-synced synthetic range image that produces a match. A central processing unit 2 is the hardware within a computer that carries out the instructions of a computer program by performing the basic arithmetical, logical, and input/output operations of the system. CPU 2 is operatively associated with memory 4, which includes data and stored instructions. CPU 2 and memory 4 are both operatively associated with the graphics processing unit 6. Graphics processing unit (GPU) 6, also known as a visual processing unit (VPU), is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the building of images in a frame buffer intended for output to a display. The memory receives the input 4, and stores software, which the CPU and GPU use to perform the processes described herein.

8 designates a motherboard, to which the CPU, memory, and GPU are electrically associated. 10 is a display monitor to display a visual depiction of the range-synced synthetic range image that produces a match. In some applications, depending on the verification requirements, a visual verification by a user is important to provide an additional layer of validation before acting on the match (for example dedicating resources to attack based on the match); therefore, some embodiments include the display monitor 10.

Article of Manufacture

Article of manufacture embodiments are directed to non-transitory processor readable medium(s) having stored thereon processor executable instructions that, when executed by the processor(s), cause the processor to perform the process(es) described herein. The term non-transitory processor readable medium include one or more non-transitory processor-readable medium (devices, carriers, or media) having stored thereon a plurality of instructions, that, when executed by the electronic processor (typically a central processing unit—an electronic circuit which executes computer programs, containing a processing unit and a control unit), cause the processor to process/manipulate/act on data according to the plurality of instructions (defined herein using the process/function form). The non-transitory medium 6 can be any non-transitory processor readable medium (media), including, for example, a magnetic storage media, “floppy disk”, CD-ROM, RAM, a PROM, an EPROM, a FLASH-EPROM, any other memory chip or cartridge, a file server providing access to the programs via a network transmission line, holographic unit, etc. Of course, those skilled in the art will recognize that many modifications may be made to this configuration without departing from the scope. In some system embodiments, the electronic processor is co-located with the processor readable medium; in other system embodiments, the electronic processor is remotely located from the processor readable medium. It is noted that the steps/acts/processes described herein including the figures can be interpreted as representing data structures or sets of instructions for causing the computer readable medium to perform the step/act/process. Identifying or ‘electronically identifying’ involves any one or more of the following: 1) associating an electronic marker, tag, or value with said target model that identifies it as a match; 2) storing data associated with said target model or the parameters of the best match hypothesis in memory at a location indicative that the hypothesis/model matches the target; 3) displaying it the synthetic range image on an visual display; or 4) any conventional means of identifying a target model as a match.

Range-Sync Embodiments—Non-specific Hypothesizer

In general, with reference to FIG. 2, rang-sync embodiments include receiving parameter values for a current target hypothesis 12, rendering a synthetic range image according to the selected parameter values of the current target hypothesis 14, generating an average range difference between the sensor target range image and the synthetic range image for the current hypothesis 15, generating a range-synced synthetic range image for the current target hypothesis 16, computing a matching score for the current target hypothesis 18, determining whether the matching score for the current target hypothesis is greater than a pre-determined threshold 20, and iterating through the selecting, rendering, rang-syncing, calculating, and determining steps for subsequent hypotheses until a target hypothesis produces a matching score greater than the pre-determined threshold 24 or the model hypothesis iteration set is completed (as determined in decision diamond 22)—in which case the target model will not be declared a match and the model hypotheses set is complete.

Each of these steps or sub-processes is treated is more detail.

Block 12 includes receiving parameter values parameters P_(g) for a target hypothesis ‘g’. The plurality of target search parameters are selected from the group of search parameters describing location, orientation, and velocity of a target within a sensor range image R generated from range data collected by a sensor. Receiving parameter values for a hypothesis P_(g) is treated in detail supra.

Once the input information is received, a synthetic range image R^(s,g) is generated for the target hypothesis ‘g’ by rendering a 3-D wire grid model (that was input) at a pose defined by the parameter values P_(g), as is shown in block 14. Rendering the synthetic range image at the pose parameter values of the current hypothesis P_(g) is performed using OpenG1 according to conventional techniques and a fast graphics processing unit.

Next, with reference to block IS, embodiments include computing an average range difference a_(g) for the current hypothesis g between the sensor target range image and the synthetic range image R^(s,g) of the hypothesis being tested, according to equation 7.

$\begin{matrix} {a_{g} = \frac{\sum\limits_{i = 1}^{b}\;{\sum\limits_{j = 1}^{c}\;\begin{matrix} {{r_{ij}^{s,g} - {r_{ij}\mspace{14mu}{if}\mspace{14mu} f_{ij}^{s_{range},g}}} = {true}} \\ {{0\mspace{14mu}{if}\mspace{14mu} f_{ij}^{s_{range},g}} = {false}} \end{matrix}}}{\sum\limits_{i = 1}^{b}\;{\sum\limits_{j = 1}^{c}\;\begin{matrix} {{1\mspace{14mu}{if}\mspace{14mu} f_{ij}^{s_{range},g}} = {true}} \\ {{0\mspace{14mu}{if}\mspace{14mu} f_{ij}^{s_{range},g}} = {false}} \end{matrix}}}} & {{Equation}\mspace{14mu} 7} \end{matrix}$

Next, with reference to block 16, embodiments generate a range-synced synthetic range image for the current hypothesis R_(sync) ^(S,g) by adjusting range values of pixels in the synthetic range image for the current hypothesis according to Equation 8. R _(sync) ^(S,g)={(r _(ij) ^(s,g) −a _(g) d), i=1, . . . b, j=1 . . . c}  Equation 8 A pixel at location ij in the range-synced synthetic range image of a ‘g^(th)’ hypothesis is notationally represented herein as f_(ij) ^(S) ^(range) ^(, g)=true if the pixel is on the rendered 3-D target model in the range-synced synthetic range image of the ‘g^(th)’ hypothesis and as f_(ij) ^(S) ^(range) ^(, g)=false if it lies off the rendered 3-D target model in the range-synced synthetic range image of the ‘g^(th)’ hypothesis.

Next, with reference to block 18, a matching score for the current hypothesis geometrical score C_(g) is computed as in Equation 9, where

${T = {L\left( \frac{1}{2} \right)}^{v}},$ L and k are set experimentally based an the sensor range noise and the object modeling errors, where var2 is greater than var3, var1 value is greater than L, and d_(i,j) _(—) _(sync) ^(g) is a discrepancy between the sensor range image and range-synced synthetic range image at pixel location ij in the range-synced synthetic range image mathematically defined by Equations 10, which employs Equation 11.

$\begin{matrix} {C_{g} = {\sum\limits_{v = 0}^{k}\;{\sum\limits_{i = 0}^{b}\;{\sum\limits_{j = 0}^{c}\;\left\{ \begin{matrix} {{var}\; 2} & {d_{i,j}^{g} < T} \\ {{var}\; 3} & {d_{i,j}^{g} \geq T} \end{matrix} \right.}}}} & {{Equation}\mspace{14mu} 9} \\ {d_{i,{j\_{sync}}}^{g} = \left\{ \begin{matrix} {{Minimum}\left( \sqrt{\left( {\left( {r_{i,j}^{S,g} - {a_{g}d}} \right) - N_{ij}} \right)^{2}} \right)} & {{{if}\mspace{14mu} f_{{ij},{sync}}^{S_{range},g}} = {true}} \\ {{var}\; 1} & {{{if}\mspace{14mu} f_{{ij},{sync}}^{S_{range},g}} = {false}} \end{matrix} \right.} & {{Equation}\mspace{14mu} 10} \end{matrix}$ N _(ij)={(r _(mn) ,m),m=(i−h), . . . (i+h), n=(j−w), . . . (j+w)}  Equation 11

In some embodiments, h and w in Equation 8 are selected from the range of values between 0 and 5. In some embodiments, h and w are the same value. In some embodiments h and w are equal to 1.

When the matching score for a current hypothesis geometrical score C_(g) exceeds the threshold value, as determined in block 20, a synthetic intensity image is rendered at the target pose parameters of the current hypothesis P_(g) and an intensity matching score C_(i) is calculated (as in block 24) according to Equation 12, where k is an index that iterates over all t,i,j; T is the average intensity of all pixels in the synthetic intensity image; Ī is the average intensity of all pixels in the imagery data; I_(k) is the intensity of the current pixel in the intensity imagery; and T_(k) is the intensity of the current (ij) pixel in the synthetic intensity image if the current pixel lies on the target model in the synthetic intensity image and zero otherwise.

$\begin{matrix} {C_{i} = \frac{\sum\limits_{k = 1}^{n}\;{\left( {I_{k} - \overset{\_}{I}} \right)\left( {T_{k} - \overset{\_}{T}} \right)}}{\sqrt{{n{\sum\limits_{k = 1}^{n}\;\left( I_{k} \right)^{2}}} - \left( {\sum\limits_{k = 1}^{n}\; I_{k}} \right)^{2}}\sqrt{{n{\sum\limits_{k = 1}^{n}\;\left( T_{k} \right)^{2}}} - \left( {\sum\limits_{k = 1}^{n}\; T_{k}} \right)^{2}}}} & {{Equation}\mspace{11mu} 12} \\ {{\sum\limits_{k = 1}^{n}\;\left( T_{k} \right)^{2}} = {\sum\limits_{i = 1}^{b}\;{\sum\limits_{j = 1}^{c}\;\left\{ \mspace{11mu}\frac{{\left( T_{ij} \right)^{2}f_{i,j}^{s\_{color}}} = {true}}{{0\mspace{14mu} f_{i,j}^{s\_{color}}} = {false}} \right.}}} & {{Equation}\mspace{14mu} 13} \\ {{\sum\limits_{k = 1}^{n}\; T_{k}} = {\sum\limits_{i = 1}^{b}\;{\sum\limits_{j = 1}^{c}\;\left\{ \mspace{11mu}\frac{{T_{{ij}\;}\; f_{i,j}^{s\_{color}}} = {true}}{{0\mspace{14mu} f_{i,j}^{s\_{color}}} = {false}} \right.}}} & {{Equation}\mspace{14mu} 14} \\ {{\sum\limits_{k = 1}^{n}{I_{k}T_{k}}} = {\sum\limits_{i = 1}^{b}\;{\sum\limits_{j = 1}^{c}\left\{ \frac{{\left( {I_{ij}T_{ij}} \right)\mspace{14mu} f_{i,j}^{s\_{color}}} = {true}}{{0\mspace{14mu} f_{i,j}^{s\_{color}}} = {false}} \right.}}} & {{Equation}\mspace{14mu} 15} \\ {{\sum\limits_{k = 1}^{n}{{- T_{k}}\overset{\_}{I}}} = {\sum\limits_{i = 1}^{b}\;{\sum\limits_{j = 1}^{c}\left\{ \frac{{\left( {{- T_{ij}}\overset{\_}{I}} \right)\mspace{11mu} f_{i,j}^{s\_{color}}} = {true}}{{0\mspace{14mu} f_{i,j}^{s\_{color}}} = {false}} \right.}}} & {{Equation}\mspace{14mu} 16} \end{matrix}$

When the intensity matching score exceeds the threshold value as in block 28 the ‘object’ (sometimes a target) in the training images used to generate the template image is declared to be the ‘object’ in the intensity image of the scene (as well as the object in the range image represented by the 3-D wiregrid model as indicated by the geometrical matching score exceeding the geometrical matching threshold) 30.

When the geometrical matching score for a current hypothesis C_(g) does not exceed the threshold value, the program determines whether a subsequent hypothesis will be analyzed for the model hypotheses set, in block 22.

When another hypothesis will not be tested, the process does not declare the target model of the current model hypotheses set to be a match 26, and where only a single target model is tested, the process ends for the model hypotheses set. Where multiple models are tested, as described supra, the program would select a new target model for a different model hypotheses set, and perform blocks 12-22 for at least one hypothesis of the new target model.

Parameter Value Selection

With reference to FIG. 3, in some embodiments, the parameter values are selected according to the particulars described in this “Parameter Value Selection” section.

Input block 32 describes input information as defined above. This block indicates receipt of the data in input into memory in a form in which CPU and GPU can process. To reiterate, as described above, input information input in block 32 is: 1) sensor range image; 2) target information; 3) initial target search parameter extents; and 4) target model to be used during the current model hypotheses set. Also recall that this description will focus on iterations through a single model hypotheses set, though other embodiments include instructions and processes that proceed through multiple model hypotheses sets by performing the processes in this section on multiple input target models.

With reference to block 34, parameter values P_(g) for an initial hypothesis are set equal to the pose values derived from the target pose information that is input. So, where the target pose information input in block 32 is P_(input), the parameter values for an initial hypothesis of a model hypotheses set are represented as P_(input). For all non-initial iteration hypotheses P_(g) is determined according to blocks 48 through 58. For the purposes in this section, ‘g’ is taken to increment once a hypothesis is determined to yield a high enough Matching Score. So, using this notation, P_(g) is the set of parameter values used to test a hypothesis immediately following the hypothesis tested using the target parameter values of the immediately preceding hypothesis P_(g-1).

P_(g) for a non-initial hypothesis g is determined as follows:

-   -   With reference to block 48, determine whether the geometrical         matching score of the preceding hypothesis (C_(g-1)) is greater         than the Best Score, and         -   With reference to block 50, when geometrical matching score             C_(g-1)>BestScore,             -   Update BestScore-geometrical matching score C_(g-1); and             -   Set an intermediate parameter value for each of the                 target search parameters equal to the parameter value of                 the search parameter in the preceding iteration, where                 the set of intermediate parameter values are                 mathematically represented collectively as P_(s) _(—)                 _(int) _(g) =P_(s) _(g-1) . Using this notation, P_(s)                 _(—) _(int) _(g) ={P_(s) _(—) _(int) _(g) (a), a=1 . . .                 b}, where b is the number of search parameters. So,                 using this notation, if there are three search                 parameters, let's say the x, y, and z position, P_(s)                 _(—) _(int) _(g) (1) would be the intermediate search                 parameter value assigned to the x-position for the                 g^(th) hypothesis, P_(s) _(—) _(int) _(g) (2) would be                 the intermediate search parameter value assigned to the                 y-position for the g^(th) hypothesis, and P_(s) _(—)                 _(int) _(g) (3) would be the parameter value assigned to                 the z-position for the g^(th) hypothesis         -   With reference to block 52, when geometrical matching score             C_(g-1)≦BestScore, set an intermediate parameter value for             each of the target search parameters equal to the             corresponding values of the target search parameters of the             hypothesis that produced the highest score, mathematically             represented in Equation 17, where b is the number of search             parameters and o is the number of hypotheses tested since             BestScore was last updated.             P _(s) _(—) _(int) _(g) (a)=P _(s) _(g-(o-1)) (a), a=1 . . .             b  Equation 17         -   As a notational example, for example, if the current             hypothesis is the sixth hypothesis, 3 hypotheses have been             tested since BestScore was last updated, and there are 2             search parameters, x, and y position, the intermediate             parameter value for the x position of the sixth             hypothesis—P_(s) _(—) _(int) ₆ (1)—would be set equal to the             value of the x position parameter of the second             hypothesis—P_(s) ₂ (1)—and the intermediate parameter value             for the y position of the sixth hypothesis—P_(s) _(—) _(int)             ₆ (2)—would be set to the y position parameter value of the             second hypothesis—P_(s) ₂ (2).     -   With reference to block 54, compute a minimum and maximum search         parameter bounds and for each of the search parameters,         respectively, according to equations 18 and 19, where b is the         number of search parameters.         P _(s) _(—) _(min) _(g) (a)=P _(s) _(—) _(int) _(g) (a)−e         _(g-1)(a), a=1 . . . b  Equation 18         P _(s) _(—) _(max) _(g) (a)=P _(s) _(—) _(int) _(g) (a)+e         _(g-1)(a), a=1 . . . b  Equation 19     -   With reference to block 58, select each parameter value of the         search parameters for the current hypothesis, (the set of search         parameter values collectively referred to as P_(s) _(g) ), as a         random pick in the range (P_(s) _(—) _(min) _(g) . . . P_(s)         _(—) _(max) _(g) ) according to Equation 20, where U( ) is a         uniform probability function and 1 is set to the degree to which         a normal distributed random variable is desired.

$\begin{matrix} {P_{s_{g}} = {P_{{s\_ int}_{g}} + \frac{{\sum\limits_{1}^{l}\;\left\{ {U\left( {P_{{s\_ min}_{g}}\ldots\mspace{11mu} P_{{s\_ max}_{g}}} \right)} \right\}} - {lP}_{{s\_ int}_{g}}}{l + 1}}} & {{Equation}\mspace{14mu} 20} \end{matrix}$

-   -   For example, where there are only two search parameters, say an         x-position (first search parameter) and a y-position (second         search parameter),

P_(s_(g)) = {P_(s_(g))(1), P_(s_(g))(2)},  where ${P_{s_{g}}(1)} = {{P_{{s\_ int}_{g}}(1)} + \frac{{\sum\limits_{1}^{l}\;\left\{ {U\left( {{P_{{s\_ min}_{g}}(1)}\mspace{11mu}\ldots\mspace{11mu}{P_{{s\_ max}_{g}}(1)}} \right)} \right\}} - {{lP}_{{s\_ int}_{g}}(1)}}{l + 1}}$ ${P_{s_{g}}(2)} = {{P_{{s\_ int}_{g}}(2)} + \frac{{\sum\limits_{1}^{l}\;\left\{ {U\left( {{P_{{s\_ min}_{g}}(2)}\mspace{11mu}\ldots\mspace{11mu}{P_{{s\_ max}_{g}}(2)}} \right)} \right\}} - {{lP}_{{s\_ int}_{g}}(2)}}{l + 1}}$

-   -   Finally, with reference to block 60, the search parameter values         for the g^(th) iteration are set to the search parameter values         in combination with the constant parameter values,         mathematically collectively represented as P_(g)={P_(s) _(g) ,         P_(c)}         Example Iterations Through an Exemplary Embodiment Using         Specific Hypothesizer

An example is provided in this “Example” section to illustrate progression through multiple hypotheses of a model hypotheses set using a specific hypothesizer described above. With reference to FIG. 3A, Input Information 32 provides the following identified supra.

With reference to block 34, embodiments proceed by setting search parameter values P_(g) for an initial iteration (g=1) equal to P_(input), resulting in the search parameter values for the initial iteration P₁=P_(input).

Embodiments then set BestScore to an initial value of zero, as is shown in block 36.

With reference to block 38, embodiments then render the range image R^(s,1) using P₁, thereby generating a synthetic range image for the initial hypothesis having range values notationally represented as R^(s,1)={r_(ij) ^(s,1), i=1, . . . b, j=1 . . . c}. Rendering at a specific pose has been treated above.

With reference to block 39, embodiments then determine an average range difference for the current hypothesis a₁ between the sensor range image and the initial hypothesis synthetic range image R^(s,1) according to Equation 21.

$\begin{matrix} {a_{1} = \frac{\sum\limits_{i = 1}^{b}\;{\sum\limits_{j = 1}^{c}\;\begin{matrix} {{r_{ij}^{s,1} - {r_{ij}\mspace{14mu}{if}\mspace{14mu} f_{ij}^{s_{range},1}}} = {true}} \\ {{0\mspace{14mu}{if}\mspace{14mu} f_{ij}^{s_{range},1}} = {false}} \end{matrix}}}{\sum\limits_{i = 1}^{b}\;{\sum\limits_{j = 1}^{c}\begin{matrix} {{{1\mspace{14mu}{if}\mspace{11mu} f_{ij}^{s_{range},1}} = {true}}\;} \\ {{0\mspace{14mu}{if}\mspace{14mu} f_{ij}^{s_{range},1}} = {false}} \end{matrix}}}} & {{Equation}\mspace{14mu} 21} \end{matrix}$

Next, with reference to block 40, embodiments generate a range-synced synthetic range image for the initial hypothesis R_(sync) ^(S,1) by adjusting range values of pixels in the synthetic range image for the initial hypothesis according to Equation 22. R _(sync) ^(S,1)={(r _(ij) ^(s,1) −a ₁ d), i=1, . . . b, j=1 . . . c}  Equation 22 A pixel at location ij in the range-synced synthetic range image of the first hypothesis is notationally represented herein as f_(ij,sync) ^(S) ^(range) ^(,1)=true if the pixel is on the rendered 3-D target model in the range-synced synthetic range image of the ‘first’ hypothesis and as f_(ij,sync) ^(S) ^(range) ^(,1)==false if it lies off the rendered 3-D target model in the range-synced synthetic range image of the ‘first’ hypothesis.

Next, with reference to block 41, a geometrical matching score for the current hypothesis C₁ is computed as in Equation 15, where

${T = {L\left( \frac{1}{2} \right)}^{v}},$ L and k are set experimentally based on the sensor range noise and the object modeling errors, where var2 is greater than var3, and d_(i,j) _(—) _(sync) ¹ is a discrepancy between the sensor range image and range-synced synthetic range image at pixel location ij in the range-synced synthetic range image mathematically defined by Equations 24, which employs Equation 25.

$\begin{matrix} {C_{1} = {\sum\limits_{v = 0}^{k}\;{\sum\limits_{i = 0}^{b}\;{\sum\limits_{j = 0}^{c}\;\left\{ \begin{matrix} {{var}\; 2} & {d_{i,{j\_ sync}}^{1} < T} \\ {{var}\; 3} & {d_{i,{j\_ sync}}^{1} \geq T} \end{matrix} \right.}}}} & {{Equation}\mspace{14mu} 23} \\ {d_{i,{j\_{sync}}}^{g} = \left\{ \begin{matrix} {{Minimum}\left( \sqrt{\left( {\left( {r_{i,j}^{S,g} - {a_{g}d}} \right) - N_{ij}} \right)^{2}} \right)} & {{{if}\mspace{14mu} f_{{ij},{sync}}^{S_{range},g}} = {true}} \\ {{var}\; 1} & {{{if}\mspace{14mu} f_{{ij},{sync}}^{S_{range},g}} = {false}} \end{matrix} \right.} & {{Equation}\mspace{14mu} 24} \end{matrix}$ N _(ij)={(r _(mn) ,m), m=(i−h), . . . (i+h), n=(j−w), . . . (j+w)}  Equation 25

In some embodiments, h and w in Equation 8 are selected from the range of values between 0 and 5. In some embodiments, h and w are the same value. In some embodiments h and w are equal to 1. L is a value (in meters) in the range of (and including) 0 to 10, depending on the fineness of the sensor. k is a value in the range of (and including) 0 to 7. In some embodiments, L=3 and k=0. In other embodiments, L=8 and k=7.

For the purposes of this example, let's set a threshold value equal to 70 and a geometrical matching score for first hypothesis C₁=55. As geometrical matching score C₁ is less than the threshold value (as determined in block 44), g is incremented to ‘2’ (block 46) and a determination regarding whether to update BestScore and what value to set P_(s) _(—) _(int) ₂ is made in decision diamond 48.

As geometrical matching score C₁ is greater than zero, the process proceeds to updating the BestScore by setting it equal to geometrical matching score C₁, and setting intermediate search parameter values equal to the values of the corresponding search parameter of the immediately preceding hypothesis, mathematically represented as P_(s) _(—) _(int) ₂ =P_(s) ₁ , and shown in block 50.

The process then proceeds to computing minimum and maximum bounds for each of the search parameters as is represented in Equations 26 and 27, where e₁ is the search parameter extent input. P _(s) _(—) _(min) ₂ −P _(s) _(—) _(int) ₂ −e ₁  Equation 26 P _(s) _(—) _(max) ₂ =P _(s) _(—) _(int) ₂ +e ₁  Equation 27

The process proceeds by computing an e₂ value according to Equation 28, though this won't be used in computing P₂, as in block 56. e ₂ =e ₁×0.5  Equation 28 The process then proceeds to selecting parameter values for the second hypothesis P₂ as in block 58, according to Equation 29.

$\begin{matrix} {P_{s_{2}} = {P_{{s\_ int}_{2}} + \frac{{\sum\limits_{1}^{l}\;\left\{ {U\left( {P_{{s\_ min}_{2}}\ldots\mspace{11mu} P_{{s\_ max}_{2}}} \right)} \right\}} - {lP}_{{s\_ int}_{2}}}{l + 1}}} & {{Equation}\mspace{14mu} 29} \end{matrix}$

With reference to block 38, embodiments then render the synthetic range image for the second hypothesis R^(s,2) using P₂, thereby generating a synthetic range surface for the initial hypothesis having range values notationally represented as in Equation 30. R ^(s,2) ={r _(ij) ^(s,2) , i=1, . . . b, j=1 . . . c}  Equation 30 Rendering at a specific pose has been treated above.

With reference to block 39, embodiments then determine an average range difference for the second hypothesis a₂ between the sensor range image and the second hypothesis synthetic range image R^(s,2) as in Equation 31.

$\begin{matrix} {a_{2} = \frac{\sum\limits_{i = 1}^{b}\;{\sum\limits_{j = 1}^{c}\;\begin{matrix} {{r_{ij}^{s,2} - {r_{ij}\mspace{14mu}{if}\mspace{14mu} f_{ij}^{s_{range},2}}} = {true}} \\ {{0\mspace{14mu}{if}\mspace{14mu} f_{ij}^{s_{range},2}} = {false}} \end{matrix}}}{\sum\limits_{i = 1}^{b}\;{\sum\limits_{j = 1}^{c}\begin{matrix} {{{1\mspace{14mu}{if}\mspace{11mu} f_{ij}^{s_{range},2}} = {true}}\;} \\ {{0\mspace{14mu}{if}\mspace{14mu} f_{ij}^{s_{range},2}} = {false}} \end{matrix}}}} & {{Equation}\mspace{14mu} 31} \end{matrix}$

Next, with reference to block 40, embodiments generate a range-synced synthetic range image for the initial hypothesis R_(sync) ^(S,2) by adjusting range values of pixels in the synthetic range image for the initial hypothesis according to Equation 32. R _(sync) ^(S,2){(r _(ij) ^(s,2) −a ₂ d), i=1, . . . , j=1 . . . c}  Equation 32 A pixel at location ij in the range-synced synthetic range image of the first hypothesis is notationally represented herein as f_(ij,sync) ^(S) ^(range) ^(,2)=true if the pixel is on the rendered 3-D target model in the range-synced synthetic range image of the ‘first’ hypothesis and as f_(ij,sync) ^(S) ^(range) ^(,2)=false if it lies off the rendered 3-D target model in the range-synced synthetic range image of the ‘first’ hypothesis.

Next, with reference to block 42, a geometrical matching score for the current hypothesis C₂ is computed as in Equation 33, where

${T = {L\left( \frac{1}{2} \right)}^{v}},$ L and k are set experimentally based on the sensor range noise and the object modeling errors, where var2 is greater than var3, and d_(i,j) _(—) _(sync) ² is a discrepancy between the sensor range image and range-synced synthetic range image at pixel location ij in the range-synced synthetic range image mathematically defined by Equations 34, which employs Equation 35.

$\begin{matrix} {C_{2} = {\sum\limits_{v = 0}^{k}\;{\sum\limits_{i = 0}^{b}\;{\sum\limits_{j = 0}^{c}\;\left\{ \begin{matrix} {{var}\; 2} & {d_{i,{j\_ sync}}^{2} < T} \\ {{var}\; 3} & {d_{i,{j\_ sync}}^{2} \geq T} \end{matrix} \right.}}}} & {{Equation}\mspace{14mu} 33} \\ {d_{i,{j\_{sync}}}^{2} = \left\{ \begin{matrix} {{Minimum}\left( \sqrt{\left( {\left( {r_{i,j}^{S,2} - {a_{2}d}} \right) - N_{ij}} \right)^{2}} \right)} & {{{if}\mspace{14mu} f_{{ij},{sync}}^{S_{range},2}} = {true}} \\ {{var}\; 1} & {{{if}\mspace{14mu} f_{{ij},{sync}}^{S_{range},2}} = {false}} \end{matrix} \right.} & {{Equation}\mspace{14mu} 34} \end{matrix}$ N _(ij)={(r _(mn) ,m),m=(i−h), . . . (i+h),n=(j−w), . . . (j+w)}  Equation 35

For the purposes of this example, let's first set the geometrical matching score for the second hypothesis C₂=52. As the geometrical matching score C₂ is less than the hypothetical threshold value, g is incremented to ‘3’ and a determination regarding whether to update BestScore and what value to set P_(s) _(—) _(int) ₃ is made. As geometrical matching score C₂<BsScore (which was set to 55), P_(s) _(—) _(int) ₃ is set equal to P_(s) ₁ (because the number of hypotheses tested since BestScore was updated is 1, so 0=3, and therefore, where g=3, g−o=1). A non-mathematical way to describe the value to which the intermediate parameter value for a current non-initial iteration is set is to state that the P_(s) _(—) _(int) _(g) for a current iteration is set to the search parameter values that resulted in the highest geometrical matching score.

The process would then proceed to compute minimum and maximum bounds for the search parameters of the third hypothesis according to Equations 36 and 37. P _(s) _(—) _(min3) =P _(s) _(—) _(int) ₃ −e ₂  Equation 36 P _(s) _(—) _(max3) =P _(s) _(—) _(int) ₃ +e ₂  Equation 37

The process would then proceed by computing an e₃ value, though this won't be used in computing P₃ as follows e₃=e₂×0.5

The process would then proceed to selecting search parameter values for the second hypothesis P_(s3) as follows:

${P_{s_{3}} = {P_{{s\_ int}_{3}} + \frac{{\sum\limits_{1}^{l}\;\left\{ {U\left( {P_{{s\_ min}_{3}}\ldots\mspace{11mu} P_{{s\_ max}_{3}}} \right)} \right\}} - {l\; P_{{s\_ int}_{3}}}}{l + 1}}},{{and}\mspace{14mu}{would}\mspace{14mu}{then}}$ proceed to generating a matching score for the third hypothesis using P₃={P_(s) ₃ , P_(c)} to render a synthetic range image, range sync, and compute a matching score, at which point a decision about whether to continue would be made.

For the purposes of this example, let's now set the geometrical matching score for the second hypothesis C₂=0.75. With reference to FIG. 3B, as the geometrical matching score C₂ is greater than the hypothetical threshold value, the process would proceed to generating/color-rendering a synthetic intensity image at the current hypothesis target pose parameters as in block 80.

The process would then proceed to computing an intensity matching scare Ci for the current hypothesis as in block 82. The intensity matching score is then electronically compared to the intensity threshold value as in block 84. If the intensity matching score for the current hypothesis is greater than the intensity threshold value, the ‘object’ (sometimes a target) in the training images used to generate the template image is declared to be the ‘object’ in the intensity image of the scene (as well as the object in the range image represented by the 3-D wiregrid model as indicated by the geometrical matching score exceeding the geometrical matching threshold) as in block 86. If the intensity matching score for the current hypothesis is less than the intensity threshold value, then (though the object in the image represented by the 3-D wiregrid model is declared a match) the object in the training images used to generate the template image is not declared to be the object in the intensity image of the scene (as in block 88).

Non-Range-Sync Embodiments

Embodiments that don't range-sync simply omit the steps (and/or don't include instructions that cause the computer to perform the function/processes/steps) 15 and 16, and 39 and 40, in FIGS. 2 and 3, respectively; these embodiments compute the matching score for a hypothesis using the synthetic range image generated during the current iteration (as opposed to a range-synced synthetic range image).

While the invention has been described, disclosed, illustrated and shown in various terms of certain embodiments or modifications which it has presumed in practice, the scope of the invention is not intended to be, nor should it be deemed to be, limited thereby and such other modifications or embodiments as may be suggested by the teachings herein are particularly reserved especially as they fall within the breadth and scope of the claims here appended. 

What is claimed is:
 1. A method of identifying a target in a scene, comprising: receiving target parameter values for a plurality of target parameters for a target hypothesis, wherein said plurality of target parameters are selected from the group of parameters describing location and orientation of said target in a range image of said scene, wherein said range image R is mathematically defined as R={(r_(ij)), i=1, . . . b, j=1 . . . c}, where b represents the number of columns of pixels in said sensor range image, c represents the number of rows of pixels in said sensor range image, and r_(ij) represents the range between an object at point ij in said sensor range image and a range sensor; providing intensity imagery of said scene generated by an intensity imaging sensor; electronically generating a synthetic range image of said scene for said target hypothesis by rendering a 3-D wire grid model of a plurality of 3-D wire models of targets to be identified at said target parameter values; wherein one of said plurality of 3-D models has a surface which is a match to a surface of said target, where said synthetic range image R^(s) is mathematically defined as R^(s)={r_(ij) ^(s), i=1, . . . b, j=1 . . . c}, where b represents the number of columns of pixels in said sensor range image, c represents the number of rows of pixels in said sensor range image, and r_(ij) ^(s) represents the range between an object at point ij in said synthetic range image and said range sensor; electronically computing a geometrical matching score C for said target hypothesis, where said geometrical matching score C is mathematically defined as $C = {\sum\limits_{v = 0}^{k}\;{\sum\limits_{i = 0}^{b}\;{\sum\limits_{j = 0}^{c}\;\left\{ {\begin{matrix} {{var}\; 2} & {d_{ij} < T} \\ {{var}\; 3} & {d_{ij} \geq T} \end{matrix},{{{where}\mspace{14mu} T} = {L\left( \frac{1}{2} \right)}^{v}},} \right.}}}$  where L is a first pre-determined value, where var2 is a second pre-determined value, where var3 is a third pre-determined value, where said second pre-determined value is greater than said third pre-determined value, where k is a fourth predetermined value, where d_(ij) is a discrepancy between said sensor range image and said synthetic range image at location ij of said synthetic range image and is mathematically defined as $d_{ij} = \left\{ {\begin{matrix} {{Minimum}\left( \sqrt{\left( \left( {r_{ij}^{S} - N_{ij}} \right)^{2} \right.} \right)} & {{{if}\mspace{14mu} f_{i,j}^{S_{range}}} = {true}} \\ {{var}\; 1} & {{{if}\mspace{14mu} f_{i,j}^{S_{range}}} = {false}} \end{matrix},} \right.$  where var1 is a fifth pre-determined value, where said fifth pre-determined value is greater than or equal said first pre-determined value, where N_(ij) is mathematically defined as N_(ij)={(r_(mn),m), m=(i−h), . . . (i+h), n=(j−w), . . . (j+w)}, where h is a sixth per-determined value and w is a seventh pre-determined value; electronically determining whether said geometrical matching score C exceeds a pre-determined threshold score; generating a synthetic intensity image by color rendering said 3-D model textured by said intensity imagery at said target parameter values; electronically determining an intensity matching score according to the following equation, where k is an index that iterates over all ij; T is the average intensity of all pixels in the synthetic intensity image; Ī is the average intensity of all pixels in the imagery data; I_(k) is the intensity of the current pixel in the intensity imagery; and T_(k) is the intensity of the current (ij) pixel in the synthetic intensity image if the current pixel lies on the target model in the synthetic intensity image and zero otherwise: ${C_{i} = \frac{\sum\limits_{k = 1}^{n}\;{\left( {I_{k} - \overset{\_}{I}} \right)\left( {T_{k} - \overset{\_}{T}} \right)}}{\sqrt{{n{\sum\limits_{k = 1}^{n}\;\left( I_{k} \right)^{2}}} - \left( {\sum\limits_{k = 1}^{n}\; I_{k}} \right)^{2}}\sqrt{{n{\sum\limits_{k = 1}^{n}\;\left( T_{k} \right)^{2}}} - \left( {\sum\limits_{k = 1}^{n}\; T_{k}} \right)^{2}}}};\mspace{11mu}{and}$ electronically identifying said 3-D wiregrid model located and positioned according to said target parameter values to be a match to said target when said geometrical matching score C and said intensity matching score C_(i) exceeds associated pre-determined threshold scores.
 2. The method of claim 1, wherein h is selected from the range of values between and including zero and two; wherein w is selected from the range of values between and including zero and two.
 3. The method of claim 2, where k=0 and L=3.0.
 4. The method of claim 1, wherein said generating a synthetic range image step is performed using a graphics processing unit.
 5. The method of claim 4, where k=7 and L=8.0.
 6. The method of claim 1, where said range sensor is a laser detection and ranging (LADAR) sensor. 